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An interesting approach to analyzing neural networks that has received renewed attention is to examine the equivalent kernel of the neural network.
The Theory of Generalised Functions , chapter 7, pp. 263
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Hochreiter, S · 1991
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MacKay, D.J.C · 1992
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Schmidt, W.F., Kraaijveld, M.A., and Duin, R.P.W · 1992
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Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., and Frasconi, P · 1994
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Bayesian Learning for Neural Networks
Neal, R.M · 1994
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Probability and Measure
Billingsley, P · 1995
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Bryc, W · 1995
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Estimating equivalent kernels for neural networks: A data perturbation approach
Burgess, A.N · 1997
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Williams, C.K.I · 1997
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Gradient flow in recurrent nets: the difficulty of learning long-term dependencies
Hochreiter, S., Bengio, Y., and Frasconi, P · 2001
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Extreme learning machine: a new learning scheme of feedforward neural networks
Huang, G., Zhu, Q., and Siew, C · 2004
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Continuous neural networks
Roux, N. Le and Bengio, Y · 2007
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Random features for large-scale kernel machines
Rahimi, A. and Recht, B · 2008
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Kernel methods for deep learning
Cho, Y. and Saul, L.K · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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Understanding the difficulty of training deep feedforward neural networks
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Multivariate statistical simulation: A guide to selecting and generating continuous multivariate distributions
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Choromanska, A., Henaff, M., Mathieu, M., Arous, G.B., and LeCun, Y · 2015
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Global optimality in tensor factorization, deep learning, and beyond
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Wavenet: A generative model for raw audio
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Understanding deep learning requires rethinking generalization
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The shattered gradients problem: If resnets are the answer, then what is the question?
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Began: Boundary equilibrium generative adversarial networks
Berthelot, D., Schumm, T., and Metz, L · 2017
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Daniely, A., Frostig, R., and Singer, Y · 2016
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